Assessing utilities for muscle-invasive bladder cancer-related health states.

OBJECTIVES: How patients value functional outcomes against oncologic outcomes during decision-making for muscular-invasive bladder cancer (MIBC) remains unclear. We sought to quantify individuals' preferences on a scale of 0 to 1, where 1 represents perfect health and 0 represents death. METHODS: Descriptions of 6 hypothetical health states were developed. These included: Neoadjuvant chemotherapy followed by radical cystectomy with ileal conduit (IC) or with neobladder reconstruction (NB), Transurethral resection and chemotherapy/radiation (CRT), CRT requiring salvage cystectomy (SC), Recurrent/metastatic bladder cancer after local therapy (RMBC), and Metastatic bladder cancer (MBC). Descriptions consisted of diagnosis, treatments, adverse effects, follow-up protocol, and prognosis and were reviewed for accuracy by expert panel. Included individuals were asked to evaluate states using the visual analog scale (VAS) and standard gamble (SG) methods. RESULTS: Fifty-four individuals were included for analysis. No score differences were observed between IC, NB, and CRT on VAS or SG. On VAS, SC (value = 0.429) was rated as significantly worse (P < 0.001) than NB (value = 0.582) and CRT (value = 0.565). However, this was not the case using the SG method. Both RMBC (VAS value = 0.178, SG value = 0.631) and MBC (VAS value = 0.169, SG value = 0.327) rated as significantly worse (P < 0.001) than the other states using both VAS and SG. CONCLUSIONS: Within this sample of the general population, preferences for local treatments including IC, NB, and CRT were not found to be significantly different. These values can be used to calculate quality-adjusted life expectancy in future cost-effectiveness analyses.

Ensemble learning and tensor regularization for cone-beam computed tomography-based pelvic organ segmentation.

PURPOSE: Cone-beam computed tomography (CBCT) is a widely accessible low-dose imaging approach compatible with on-table patient anatomy observation for radiotherapy. However, its use in comprehensive anatomy monitoring is hindered by low contrast and low signal-to-noise ratio and a large presence of artifacts, resulting in difficulty in identifying organ and structure boundaries either manually or automatically. In this study, we propose and develop an ensemble deep-learning model to segment post-prostatectomy organs automatically. METHODS: We utilize the ensemble logic in various modules during the segmentation process to alleviate the impact of low image quality of CBCT. Specifically, (1) semantic attention was obtained from an ensemble 2.5D You-only-look-once detector to consistently define regions of interest, (2) multiple view-specific two-stream 2.5D segmentation networks were developed, using auxiliary high-quality CT data to aid CBCT segmentation, and (3) a novel tensor-regularized ensemble scheme was proposed to aggregate the estimates from multiple views and regularize the spatial integrity of the final segmentation. RESULTS: A cross-validation study achieved Dice similarity coefficient and mean surface distance of 0.779 ± $\pm$ 0.069 and 2.895 ± $\pm$ 1.496 mm for the rectum, and 0.915 ± $\pm$ 0.055 and 1.675 ± $\pm$ 1.311 mm for the bladder. CONCLUSIONS: The proposed ensemble scheme manages to enhance the geometric integrity and robustness of the contours derived from CBCT with light network components. The tensor regularization approach generates organ results conforming to anatomy and physiology, without compromising typical quantitative performance in Dice similarity coefficient and mean surface distance, to support further clinical interpretation and decision making.

Systematic feasibility analysis of performing elastography using reduced dose CT lung image pairs.

PURPOSE: Elastography using computer tomography (CT) is a promising methodology that can provide patient-specific regional distributions of lung biomechanical properties. The purpose of this paper is to investigate the feasibility of performing elastography using simulated lower dose CT scans. METHODS: A cohort of eight patient CT image pairs were acquired with a tube current-time product of 40 mAs for estimating baseline lung elastography results. Synthetic low mAs CT scans were generated from the baseline scans to simulate the additional noise that would be present in acquisitions at 30, 25, and 20 mAs, respectively. For the simulated low mAs scans, exhalation and inhalation datasets were registered using an in-house optical flow deformable image registration algorithm. The registered deformation vector fields (DVFs) were taken to be ground truth for the elastography process. A model-based elasticity estimation was performed for each of the reduced mAs datasets, in which the goal was to optimize the elasticity distribution that best represented their respective DVFs. The estimated elasticity and the DVF distributions of the reduced mAs scans were then compared with the baseline elasticity results for quantitative accuracy purposes. RESULTS: The DVFs for the low mAs and baseline scans differed from each other by an average of 1.41 mm, which can be attributed to the noise added by the simulated reduction in mAs. However, the elastography results using the DVFs from the reduced mAs scans were similar from the baseline results, with an average elasticity difference of 0.65, 0.71, and 0.76 kPa, respectively. This illustrates that elastography can provide equivalent results using low-dose CT scans. CONCLUSIONS: Elastography can be performed equivalently using CT image pairs acquired with as low as 20 mAs. This expands the potential applications of CT-based elastography.

Feasibility of deriving a novel imaging biomarker based on patient-specific lung elasticity for characterizing the degree of COPD in lung SBRT patients.

OBJECTIVE:: Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity. METHODS:: We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA950 using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis. RESULTS:: The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA950. Along with acting as an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA950. CONCLUSIONS:: Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups. ADVANCES IN KNOWLEDGE:: The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.

Initial clinical observations of intra- and interfractional motion variation in MR-guided lung SBRT.

OBJECTIVE: To evaluate variations in intra- and interfractional tumour motion, and the effect on internal target volume (ITV) contour accuracy, using deformable image registration of real-time two-dimensional-sagittal cine-mode MRI acquired during lung stereotactic body radiation therapy (SBRT) treatments. METHODS: Five lung tumour patients underwent free-breathing SBRT treatments on the ViewRay system, with dose prescribed to a planning target volume (defined as a 3-6 mm expansion of the 4DCT-ITV). Sagittal slice cine-MR images (3.5 × 3.5 mm2 pixels) were acquired through the centre of the tumour at 4 frames per second throughout the treatments (3-4 fractions of 21-32 min). Tumour gross tumour volumes (GTVs) were contoured on the first frame of the MR cine and tracked for the first 20 min of each treatment using offline optical-flow based deformable registration implemented on a GPU cluster. A ground truth ITV (MR-ITV20 min) was formed by taking the union of tracked GTV contours. Pseudo-ITVs were generated from unions of the GTV contours tracked over 10 s segments of image data (MR-ITV10 s). RESULTS: Differences were observed in the magnitude of median tumour displacement between days of treatments. MR-ITV10 s areas were as small as 46% of the MR-ITV20 min. CONCLUSION: An ITV offers a "snapshot" of breathing motion for the brief period of time the tumour is imaged on a specific day. Real-time MRI over prolonged periods of time and over multiple treatment fractions shows that ITV size varies. Further work is required to investigate the dosimetric effect of these results. Advances in knowledge: Five lung tumour patients underwent free-breathing MRI-guided SBRT treatments, and their tumours tracked using deformable registration of cine-mode MRI. The results indicate that variability of both intra- and interfractional breathing amplitude should be taken into account during planning of lung radiotherapy.

Estimation and validation of patient-specific high-resolution lung elasticity derived from 4DCT.

PURPOSE: Lung diseases are commonly associated with changes in lung tissue's biomechanical properties. Functional imaging techniques, such as elastography, have shown great promise in measuring tissue's biomechanical properties, which could expand the utility and effectiveness of radiotherapy treatment planning. We present a novel methodology for characterizing a key biomechanical property, parenchymal elasticity, derived solely from 4DCT datasets. METHODS: Specifically, end-inhalation and end-exhalation breathing phases of the 4DCT datasets were deformably registered and the resulting displacement maps were considered to be ground-truth. A mid-exhalation image was also prepared for verification purposes. A GPU-based biomechanical model was then generated from the patient end-exhalation dataset and used as a forward model to iteratively solve for the elasticity distribution. Displacements at the surface of the lungs were applied as boundary constraints for the model-guided tissue elastography, while the inner voxels were allowed to deform according to the linear elastic forces within the biomechanical model. A convergence criteria of 10% maximum deformation was employed for the iterative process. RESULTS: The lung tissue elasticity estimation was documented for a set of 15 4DCT patient datasets. Maximum lung deformations were observed to be between 6 and 31 mm. Our results showed that, on average, 89.91 ± 4.85% convergence was observed. A validation study consisting of mid-exhalation breathing phases illustrated an accuracy of 87.13 ± 10.62%. Structural similarity, normalized cross-correlation, and mutual information were used to quantify the degree of similarity between the following image pairs: (a) the model-generated end-exhalation and ground-truth end-exhalation, and (b) model-generated mid-exhalation images and ground-truth mid-exhalation. CONCLUSIONS: Overall, the results suggest that the lung elasticity can be measured with approximately 90% convergence using routinely acquired clinical 4DCT scans, indicating the potential for a lung elastography implementation within the radiotherapy clinical workflow. The regional lung elasticity found here can lead to improved tissue sparing radiotherapy treatment plans, and more precise monitoring of treatment response.

A neural network approach for fast, automated quantification of DIR performance.

PURPOSE: A critical step in adaptive radiotherapy (ART) workflow is deformably registering the simulation CT with the daily or weekly volumetric imaging. Quantifying the deformable image registration accuracy under these circumstances is a complex task due to the lack of known ground-truth landmark correspondences between the source data and target data. Generating landmarks manually (using experts) is time-consuming, and limited by image quality and observer variability. While image similarity metrics (ISM) may be used as an alternative approach to quantify the registration error, there is a need to characterize the ISM values by developing a nonlinear cost function and translate them to physical distance measures in order to enable fast, quantitative comparison of registration performance. METHODS: In this paper, we present a proof-of-concept methodology for automated quantification of DIR performance. A nonlinear cost function was developed as a combination of ISM values and governed by the following two expectations for an accurate registration: (a) the deformed data obtained from transforming the simulation CT data with the deformation vector field (DVF) should match the target image data with near perfect similarity, and (b) the similarity between the simulation CT and deformed data should match the similarity between the simulation CT and the target image data. A deep neural network (DNN) was developed that translated the cost function values to actual physical distance measure. To train the neural network, patient-specific biomechanical models of the head-and-neck anatomy were employed. The biomechanical model anatomy was systematically deformed to represent changes in patient posture and physiological regression. Volumetric source and target images with known ground-truth deformations vector fields were then generated, representing the daily or weekly imaging data. Annotated data was then fed through a supervised machine learning process, iteratively optimizing a nonlinear model able to predict the target registration error (TRE) for given ISM values. The cost function for sub-volumes enclosing critical radiotherapy structures in the head-and-neck region were computed and compared with the ground truth TRE values. RESULTS: When examining different combinations of registration parameters for a single DIR, the neural network was able to quantify DIR error to within a single voxel for 95% of the sub-volumes examined. In addition, correlations between the neural network predicted error and the ground-truth TRE for the Planning Target Volume and the parotid contours were consistently observed to be > 0.9. For variations in posture and tumor regression for 10 different patients, patient-specific neural networks predicted the TRE to within a single voxel > 90% on average. CONCLUSIONS: The formulation presented in this paper demonstrates the ability for fast, accurate quantification of registration performance. DNN provided the necessary level of abstraction to estimate a quantified TRE from the ISM expectations described above, when sufficiently trained on annotated data. In addition, biomechanical models facilitated the DNN with the required variations in the patient posture and physiological regression. With further development and validation on clinical patient data, such networks have potential impact in patient and site-specific optimization, and stream-lining clinical registration validation.

Multi-Kinect v2 Camera Based Monitoring System for Radiotherapy Patient Safety.

3D kinect camera systems are essential for real-time imaging of 3D treatment space that consists of both the patient anatomy as well as the treatment equipment setup. In this paper, we present the technical details of a 3D treatment room monitoring system that employs a scalable number of calibrated and coregistered Kinect v2 cameras. The monitoring system tracks radiation gantry and treatment couch positions, and tracks the patient and immobilization accessories. The number and positions of the cameras were selected to avoid line-of-sight issues and to adequately cover the treatment setup. The cameras were calibrated with a calibration error of 0.1 mm. Our tracking system evaluation show that both gantry and patient motion could be acquired at a rate of 30 frames per second. The transformations between the cameras yielded a 3D treatment space accuracy of < 2 mm error in a radiotherapy setup within 500mm around the isocenter.

Near Real-Time Assessment of Anatomic and Dosimetric Variations for Head and Neck Radiation Therapy via Graphics Processing Unit-based Dose Deformation Framework.

PURPOSE: The purpose of this study was to systematically monitor anatomic variations and their dosimetric consequences during intensity modulated radiation therapy (IMRT) for head and neck (H&N) cancer by using a graphics processing unit (GPU)-based deformable image registration (DIR) framework. METHODS AND MATERIALS: Eleven IMRT H&N patients undergoing IMRT with daily megavoltage computed tomography (CT) and weekly kilovoltage CT (kVCT) scans were included in this analysis. Pretreatment kVCTs were automatically registered with their corresponding planning CTs through a GPU-based DIR framework. The deformation of each contoured structure in the H&N region was computed to account for nonrigid change in the patient setup. The Jacobian determinant of the planning target volumes and the surrounding critical structures were used to quantify anatomical volume changes. The actual delivered dose was calculated accounting for the organ deformation. The dose distribution uncertainties due to registration errors were estimated using a landmark-based gamma evaluation. RESULTS: Dramatic interfractional anatomic changes were observed. During the treatment course of 6 to 7 weeks, the parotid gland volumes changed up to 34.7%, and the center-of-mass displacement of the 2 parotid glands varied in the range of 0.9 to 8.8 mm. For the primary treatment volume, the cumulative minimum and mean and equivalent uniform doses assessed by the weekly kVCTs were lower than the planned doses by up to 14.9% (P=.14), 2% (P=.39), and 7.3% (P=.05), respectively. The cumulative mean doses were significantly higher than the planned dose for the left parotid (P=.03) and right parotid glands (P=.006). The computation including DIR and dose accumulation was ultrafast (∼45 seconds) with registration accuracy at the subvoxel level. CONCLUSIONS: A systematic analysis of anatomic variations in the H&N region and their dosimetric consequences is critical in improving treatment efficacy. Nearly real-time assessment of anatomic and dosimetric variations is feasible using the GPU-based DIR framework. Clinical implementation of this technology may enable timely plan adaptation and improved outcome.

A framework for modeling and visualizing cardiovascular deformation under normal and altered circulatory conditions.

The aim of this paper is to model and visualize cardiovascular deformations in order to better understand vascular movements inside the lung and heart caused by abnormal cardiac conditions. The modeling was performed in two steps: first step involved modeling the cardiac output taking into account of the heart rate and preload blood volume, contractility and systematic vascular resistance. The second step involved deforming a 3D cine cardiac gated Magnetic Resonance Volume to the corresponding cardiac output. Cardiac-gated MR imaging of 4 healthy volunteers were acquired. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 second breath-hold maneuvers. A 3D multi-resolution optical flow deformable image registration algorithm was used to quantify the volumetric cardiovascular displacements for known cardiac outputs. Results show that a real-time visualization of the vascular deformations inside both the lung as well as the heart can be seen for different cardiac outputs representing normal and abnormal cardiac conditions.

Modeling and incorporating cardiac-induced lung tissue motion in a breathing motion model.

PURPOSE: The purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model. METHODS: The authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement y(X0) of each voxel (X0) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow. RESULTS: The average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement. CONCLUSIONS: The authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.

4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.

PURPOSE: The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations. METHODS: The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in 2 mm(3) window range. RESULTS: Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values <3 mm. In addition, the average number of voxel that failed the Gamma criteria was <3 %, which supports the clinical applicability of the propose registration mechanism. CONCLUSION: The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.

Multi-scale, multi-modal image integration for image-guided clinical interventions in the head and neck anatomy.

The aim of this paper is to enable model guided multi-scale and multi-modal image integration for the head and neck anatomy. The image modality used for this purpose includes multi-pose Magnetic Resonance Imaging (MRI), Mega Voltage CT, and hand-held Optical Coherence Tomography. A biomechanical model that incorporates subject-specific young's modulus and shear modulus properties is developed from multi-pose MRI, positioned in the treatment setup using Mega Voltage CT (MVCT), and actuated using multiple kinect surface cameras to mimic patient postures during Optical Coherence Microscopy (OCM) imaging. Two different 3D tracking mechanisms were employed for aligning the patient surface and the probe position to the MRI data. The results show the accuracy of the two tracking algorithms and the 3D head and neck deformation representing the multiple poses, the subject will take during the OCM imaging.

A client-server framework for 3D remote visualization of radiotherapy treatment space.

Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While trained radiation therapists conduct patient positioning, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi-3D camera framework was used for acquiring the 3D treatment space. A client-server framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the server side to enable hardware flexibility on the client side. On the server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi graphics processing units (GPU) system. The rendered 3D images were then encoded using a GPU-based H.264 encoding for streaming. Results showed that for a stereo image size of 1280 × 960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mbps network, the treatment space visualization occurred at 8-40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments.

Computational fluid dynamics modeling of airflow inside lungs using heterogenous anisotropic lung tissue elastic properties.

The aim of this paper is to model the airflow inside lungs during breathing and its fluid-structure interaction with the lung tissues and the lung tumor using subject-specific elastic properties. The fluid-structure interaction technique simultaneously simulates flow within the airway and anisotropic deformation of the lung lobes. The three-dimensional (3D) lung geometry is reconstructed from the end-expiration 3D CT scan datasets of humans with lung cancer. The lung is modeled as a poro-elastic medium with anisotropic elastic property (non-linear Young's modulus) obtained from inverse lung elastography of 4D CT scans for the same patients. The predicted results include the 3D anisotropic lung deformation along with the airflow pattern inside the lungs. The effect is also presented of anisotropic elasticity on both the spatio-temporal volumetric lung displacement and the regional lung hysteresis.

Modeling Airflow Using Subject-Specific 4DCT-Based Deformable Volumetric Lung Models.

Lung radiotherapy is greatly benefitted when the tumor motion caused by breathing can be modeled. The aim of this paper is to present the importance of using anisotropic and subject-specific tissue elasticity for simulating the airflow inside the lungs. A computational-fluid-dynamics (CFD) based approach is presented to simulate airflow inside a subject-specific deformable lung for modeling lung tumor motion and the motion of the surrounding tissues during radiotherapy. A flow-structure interaction technique is employed that simultaneously models airflow and lung deformation. The lung is modeled as a poroelastic medium with subject-specific anisotropic poroelastic properties on a geometry, which was reconstructed from four-dimensional computed tomography (4DCT) scan datasets of humans with lung cancer. The results include the 3D anisotropic lung deformation for known airflow pattern inside the lungs. The effects of anisotropy are also presented on both the spatiotemporal volumetric lung displacement and the regional lung hysteresis.

Visualization of 3D volumetric lung dynamics for real-time external beam lung radiotherapy.

This paper reports on the usage of physics-based 3D volumetric lung dynamic models for visualizing and monitoring the radiation dose deposited on the lung of a human subject during lung radiotherapy. The dynamic model of each subject is computed from a 4D Computed Tomography (4DCT) imaging acquired before the treatment. The 3D lung deformation and the radiation dose deposited are computed using Graphics Processing Units (GPU). Additionally, using the dynamic lung model, the airflow inside the lungs during the treatment is also investigated. Results show the radiation dose deposited on the lung tumor as well as the surrounding tissues, the combination of which is patient-specific and varies from one treatment fraction to another.

A GPU-based framework for modeling real-time 3D lung tumor conformal dosimetry with subject-specific lung tumor motion.

In this paper, we present a graphics processing unit (GPU)-based simulation framework to calculate the delivered dose to a 3D moving lung tumor and its surrounding normal tissues, which are undergoing subject-specific lung deformations. The GPU-based simulation framework models the motion of the 3D volumetric lung tumor and its surrounding tissues, simulates the dose delivery using the dose extracted from a treatment plan using Pinnacle Treatment Planning System, Phillips, for one of the 3DCTs of the 4DCT and predicts the amount and location of radiation doses deposited inside the lung. The 4DCT lung datasets were registered with each other using a modified optical flow algorithm. The motion of the tumor and the motion of the surrounding tissues were simulated by measuring the changes in lung volume during the radiotherapy treatment using spirometry. The real-time dose delivered to the tumor for each beam is generated by summing the dose delivered to the target volume at each increase in lung volume during the beam delivery time period. The simulation results showed the real-time capability of the framework at 20 discrete tumor motion steps per breath, which is higher than the number of 4DCT steps (approximately 12) reconstructed during multiple breathing cycles.